Our work fits this ambition to help a domain expert user get involved in data analysis operations, especially in learning tasks. Therefore, obtaining explanations for predictive models in an efficient manner, whether in terms of accuracy or computation time, is essential. In a previous work [1]}, we proved the feasibility of lowering the computation time of existing solutions with a limited loss of explanation accuracy.
Finding more efficient approximations of these solutions, thanks to the detection of more relevant coalitions of attributes was the objective of our previous work in [2]}. This paper extends these proposals by detailing and adding examples for each of the coalitional proposals. A new comparison with one of the most used method of the literature, SHAP [3]}, is presented. In particular, our experiments offer a complete view of the performances (in time and error scores) between all the methods as well as a better characterization of the groups of attributes generated. A real use case, regarding the SARS-COV2 data, illustrates the relevance of our coalitional explanations.
